Add samples.

codemzs · codemzs · commit 9e9ae1382a02 · 2019-06-28T14:10:53.000-07:00
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs
@@ -40,7 +40,7 @@ public static void Example()
             ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
             // Create a time series prediction engine from the model.
-            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
             Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
             Console.WriteLine("Data\tAlert\tScore\tMag");
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
@@ -57,7 +57,7 @@ public static void Example()
             ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
             // Create a prediction engine from the model for feeding new data.
-            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
             // Start streaming new data points with no change point to the prediction engine.
             Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
                 model = ml.Model.Load(file, out DataViewSchema schema);
 
             // We must create a new prediction engine from the persisted model.
-            engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+            engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
             // Run predictions on the loaded model.
             for (int i = 0; i < 5; i++)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs
@@ -57,7 +57,7 @@ public static void Example()
             ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
             // Create a prediction engine from the model for feeding new data.
-            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
             // Start streaming new data points with no change point to the prediction engine.
             Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
                 model = ml.Model.Load(stream, out DataViewSchema schema);
 
             // We must create a new prediction engine from the persisted model.
-            engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+            engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
             // Run predictions on the loaded model.
             for (int i = 0; i < 5; i++)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
@@ -56,7 +56,7 @@ public static void Example()
             ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
             // Create a time series prediction engine from the model.
-            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
             Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
             Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
                 model = ml.Model.Load(file, out DataViewSchema schema);
 
             // Create a time series prediction engine from the checkpointed model.
-            engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+            engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, ChangePointPrediction>(ml);
             for (int index = 0; index < 8; index++)
             {
                 // Anomaly change point detection.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
@@ -48,7 +48,7 @@ public static void Example()
             ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
             // Create a time series prediction engine from the model.
-            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
             Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
             Console.WriteLine("Data\tAlert\tScore\tP-Value");
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
@@ -54,7 +54,7 @@ public static void Example()
             ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
             // Create a prediction engine from the model for feeding new data.
-            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
             // Start streaming new data points with no change point to the prediction engine.
             Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
                 model = ml.Model.Load(file, out DataViewSchema schema);
 
             // We must create a new prediction engine from the persisted model.
-            engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+            engine = model.CreateAnomalyDetectionEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
             // Run predictions on the loaded model.
             for (int i = 0; i < 5; i++)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs
@@ -1,8 +1,8 @@
-/*using System;
+using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;*/
 
 namespace Samples.Dynamic
 {
@@ -12,12 +12,11 @@ public static class Forecasting
         // does forecasting.
         public static void Example()
         {
-            /*// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
             // as well as the source of randomness.
             var ml = new MLContext();
 
-            // Generate sample series data with a recurring pattern
-            const int SeasonalitySize = 5;
+            // Generate sample series data with a recurring pattern.
             var data = new List<TimeSeriesData>()
             {
                 new TimeSeriesData(0),
@@ -46,41 +45,56 @@ public static void Example()
             var inputColumnName = nameof(TimeSeriesData.Value);
 
             // Instantiate the forecasting model.
-            var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
-                1, AdaptiveSingularSpectrumSequenceForecasting.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+            var model = ml.Forecasting.ForecastBySsa(inputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
             // Train.
-            model.Train(dataView);
+            var transformer = model.Fit(dataView);
 
             // Forecast next five values.
-            var forecast = model.Forecast(5);
+            var forecastEngine = transformer.CreateForecastingEngine<TimeSeriesData>(ml);
+            var forecast = ml.Data.CreateEnumerable<ForecastResult>(forecastEngine.Forecast(5), false);
             Console.WriteLine($"Forecasted values:");
             Console.WriteLine("[{0}]", string.Join(", ", forecast));
             // Forecasted values:
             // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
 
             // Update with new observations.
-            dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
-            model.Update(dataView);
+            forecastEngine.Update(new TimeSeriesData(0));
+            forecastEngine.Update(new TimeSeriesData(0));
+            forecastEngine.Update(new TimeSeriesData(0));
+            forecastEngine.Update(new TimeSeriesData(0));
 
             // Checkpoint.
-            ml.Model.SaveForecastingModel(model, "model.zip");
+            forecastEngine.CheckPoint(ml, "model.zip");
 
             // Load the checkpointed model from disk.
-            var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+            // Load the model.
+            ITransformer modelCopy;
+            using (var file = File.OpenRead("model.zip"))
+                modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+            // We must create a new prediction engine from the persisted model.
+            var forecastEngineCopy = modelCopy.CreateForecastingEngine<TimeSeriesData>(ml);
 
             // Forecast with the checkpointed model loaded from disk.
-            forecast = modelCopy.Forecast(5);
+            forecast = ml.Data.CreateEnumerable<ForecastResult>(forecastEngineCopy.Forecast(5), false);
             Console.WriteLine("[{0}]", string.Join(", ", forecast));
             // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
 
             // Forecast with the original model(that was checkpointed to disk).
-            forecast = model.Forecast(5);
+            forecast = ml.Data.CreateEnumerable<ForecastResult>(forecastEngine.Forecast(5), false);
             Console.WriteLine("[{0}]", string.Join(", ", forecast));
-            // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]*/
+            // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
 
         }
 
+        class ForecastResult
+        {
+#pragma warning disable CS0649
+            public float Forecast;
+#pragma warning restore CS0649
+        }
+
         class TimeSeriesData
         {
             public float Value;
diff --git a/docs/samples/Microsoft.ML.Samples/Program.cs b/docs/samples/Microsoft.ML.Samples/Program.cs
@@ -1,5 +1,6 @@
 ﻿using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {
@@ -9,7 +10,7 @@ public static class Program
 
         internal static void RunAll()
         {
-            int samples = 0;
+            /*int samples = 0;
             foreach (var type in Assembly.GetExecutingAssembly().GetTypes())
             {
                 var sample = type.GetMethod("Example", BindingFlags.Public | BindingFlags.Static | BindingFlags.FlattenHierarchy);
@@ -22,7 +23,8 @@ internal static void RunAll()
                 }
             }
 
-            Console.WriteLine("Number of samples that ran without any exception: " + samples);
+            Console.WriteLine("Number of samples that ran without any exception: " + samples);*/
+            Forecasting.Example();
         }
     }
 }
diff --git a/src/Microsoft.ML.TimeSeries/AdaptiveSingularSpectrumSequenceModeler.cs b/src/Microsoft.ML.TimeSeries/AdaptiveSingularSpectrumSequenceModeler.cs
@@ -230,7 +230,7 @@ public AdaptiveSingularSpectrumSequenceModelerInternal(IHostEnvironment env, int
             _host = env.Register(LoaderSignature);
             _host.CheckParam(windowSize >= 2, nameof(windowSize), "The window size should be at least 2."); // ...because otherwise we have nothing to autoregress on
             _host.CheckParam(seriesLength > windowSize, nameof(seriesLength), "The series length should be greater than the window size.");
-            _host.Check(trainSize > 2 * windowSize, "The input series length for training should be greater than twice the window size.");
+            _host.Check(trainSize > 2 * windowSize, "The input size for training should be greater than twice the window size.");
             _host.CheckParam(0 <= discountFactor && discountFactor <= 1, nameof(discountFactor), "The discount factor should be in [0,1].");
             _host.CheckParam(maxGrowth == null || maxGrowth.Value.TimeSpan > 0, nameof(GrowthRatio.TimeSpan), "The time span must be strictly positive.");
             _host.CheckParam(maxGrowth == null || maxGrowth.Value.Growth >= 0, nameof(GrowthRatio.Growth), "The growth must be non-negative.");
diff --git a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
@@ -385,7 +385,7 @@ public static class PredictionFunctionExtensions
         /// ]]>
         /// </format>
         /// </example>
-        public static TimeSeriesAnomalyDetection<TSrc, TDst> CreateTimeSeriesPredictionFunction<TSrc, TDst>(this ITransformer transformer, IHostEnvironment env,
+        public static TimeSeriesAnomalyDetection<TSrc, TDst> CreateAnomalyDetectionEngine<TSrc, TDst>(this ITransformer transformer, IHostEnvironment env,
             bool ignoreMissingColumns = false, SchemaDefinition inputSchemaDefinition = null, SchemaDefinition outputSchemaDefinition = null)
             where TSrc : class
             where TDst : class, new()
@@ -416,7 +416,7 @@ public static TimeSeriesAnomalyDetection<TSrc, TDst> CreateTimeSeriesPredictionF
         /// ]]>
         /// </format>
         /// </example>
-        public static TimeSeriesForecasting<TSrc> CreateTimeSeriesForecastingEngine<TSrc>(this ITransformer transformer, IHostEnvironment env,
+        public static TimeSeriesForecasting<TSrc> CreateForecastingEngine<TSrc>(this ITransformer transformer, IHostEnvironment env,
             bool ignoreMissingColumns = false, SchemaDefinition inputSchemaDefinition = null, SchemaDefinition outputSchemaDefinition = null)
             where TSrc : class
         {
diff --git a/src/Microsoft.ML.TimeSeries/SSaForecasting.cs b/src/Microsoft.ML.TimeSeries/SSaForecasting.cs
@@ -285,7 +285,9 @@ internal SsaForecastingEstimator(IHostEnvironment env,
                 MaxGrowth = maxGrowth,
                 ConfidenceLevel = confidenceLevel,
                 ForcastingConfidentLowerBoundColumnName = forcastingConfidentLowerBoundColumnName,
-                ForcastingConfidentUpperBoundColumnName = forcastingConfidentUpperBoundColumnName
+                ForcastingConfidentUpperBoundColumnName = forcastingConfidentUpperBoundColumnName,
+                SeriesLength = seriesLength,
+                TrainSize = trainSize
             })
         {
         }
diff --git a/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs b/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs
@@ -204,7 +204,7 @@ public void ChangePointDetectionWithSeasonalityPredictionEngineNoColumn()
             var model = pipeline.Fit(dataView);
 
             //Create prediction function.
-            var engine = model.CreateTimeSeriesPredictionFunction<Data, Prediction1>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<Data, Prediction1>(ml);
 
             //Checkpoint with no inputs passed at prediction.
             var modelPath = "temp.zip";
@@ -217,7 +217,7 @@ public void ChangePointDetectionWithSeasonalityPredictionEngineNoColumn()
                 model2 = ml.Model.Load(file, out var schema);
 
             //Raw score after state gets updated with two inputs.
-            var engine2 = model2.CreateTimeSeriesPredictionFunction<Data, Prediction>(ml);
+            var engine2 = model2.CreateAnomalyDetectionEngine<Data, Prediction>(ml);
             var prediction2 = engine2.Predict(new Data(1));
             //Raw score after first input.
             Assert.Equal(1.1661833524703979, prediction2.Change[1], precision: 5); // Raw score
@@ -238,7 +238,7 @@ public void ChangePointDetectionWithSeasonalityPredictionEngineNoColumn()
 
             //Load the model with state updated with just one input, then pass in the second input
             //and raw score should match the raw score obtained by passing the two input in the first model.
-            var engine3 = model3.CreateTimeSeriesPredictionFunction<Data, Prediction>(ml);
+            var engine3 = model3.CreateAnomalyDetectionEngine<Data, Prediction>(ml);
             var prediction3 = engine3.Predict(new Data(1));
             Assert.Equal(0.12216401100158691, prediction2.Change[1], precision: 5); // Raw score
         }
@@ -281,7 +281,7 @@ public void ChangePointDetectionWithSeasonalityPredictionEngine()
             var model = pipeline.Fit(dataView);
             
             //Model 1: Prediction #1.
-            var engine = model.CreateTimeSeriesPredictionFunction<Data, Prediction>(ml);
+            var engine = model.CreateAnomalyDetectionEngine<Data, Prediction>(ml);
             var prediction = engine.Predict(new Data(1));
             Assert.Equal(0, prediction.Change[0], precision: 7); // Alert
             Assert.Equal(1.1661833524703979, prediction.Change[1], precision: 5); // Raw score
@@ -305,7 +305,7 @@ public void ChangePointDetectionWithSeasonalityPredictionEngine()
                 model2 = ml.Model.Load(file, out var schema);
 
             //Predict and expect the same result after checkpointing(Prediction #2).
-            engine = model2.CreateTimeSeriesPredictionFunction<Data, Prediction>(ml);
+            engine = model2.CreateAnomalyDetectionEngine<Data, Prediction>(ml);
             prediction = engine.Predict(new Data(1));
             Assert.Equal(0, prediction.Change[0], precision: 7); // Alert
             Assert.Equal(0.12216401100158691, prediction.Change[1], precision: 5); // Raw score
@@ -401,7 +401,7 @@ public void SsaForecastPredictionEngine()
             var model = new SsaForecastingEstimator(ml, args).Fit(dataView);
 
             //Model 1: Prediction #1.
-            var engine = model.CreateTimeSeriesForecastingEngine<Data>(ml);
+            var engine = model.CreateForecastingEngine<Data>(ml);
             engine.Update(new Data(1));
             engine.Update(new Data(2));
             var forecast = engine.Forecast(horizon: 5);

Original file line number	Diff line number	Diff line change
`@@ -1,5 +1,6 @@`
`1`	`1`	`using System;`
`2`	`2`	`using System.Reflection;`
	`3`	`+using Samples.Dynamic;`
`3`	`4`
`4`	`5`	`namespace Microsoft.ML.Samples`
`5`	`6`	`{`
`@@ -9,7 +10,7 @@ public static class Program`
`9`	`10`
`10`	`11`	`internal static void RunAll()`
`11`	`12`	`{`
`12`		`- int samples = 0;`
	`13`	`+ /*int samples = 0;`
`13`	`14`	`foreach (var type in Assembly.GetExecutingAssembly().GetTypes())`
`14`	`15`	`{`
`15`	`16`	`var sample = type.GetMethod("Example", BindingFlags.Public \| BindingFlags.Static \| BindingFlags.FlattenHierarchy);`
`@@ -22,7 +23,8 @@ internal static void RunAll()`
`22`	`23`	`}`
`23`	`24`	`}`
`24`	`25`
`25`		`- Console.WriteLine("Number of samples that ran without any exception: " + samples);`
	`26`	`+ Console.WriteLine("Number of samples that ran without any exception: " + samples);*/`
	`27`	`+ Forecasting.Example();`
`26`	`28`	`}`
`27`	`29`	`}`
`28`	`30`	`}`